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    Non-invasive arterial input function estimation using an MRA atlas and machine learning by Rajat Vashistha, Hamed Moradi, Amanda Hammond, Kieran O’Brien, Axel Rominger, Hasan Sari, Kuangyu Shi, Viktor Vegh, David Reutens

    Published 2025-05-01
    “…Abstract Background Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF) conventionally obtained from arterial blood samples. …”
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  9. 529

    Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks by Haoyu Wang, Sheng Cui, Changjian Ke, Chenglong Yu, Zi Liang, Deming Liu

    Published 2022-01-01
    “…In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. …”
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  10. 530

    Lateralization of memory function in temporal lobe epilepsy using scene memory fMRI by William S. Tackett, Dawn Mechanic‐Hamilton, Sandhitsu Das, Marissa Mojena, Joel M. Stein, Kathryn A. Davis, John A. Detre

    Published 2024-12-01
    “…Building upon prior work using complex scene encoding to map memory function, we demonstrate that the use of high‐resolution fMRI along with an optimized task paradigm allows memory activation to be detected within the hippocampus. …”
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  11. 531

    The Metabolism of Creatinine and Its Usefulness to Evaluate Kidney Function and Body Composition in Clinical Practice by Marcela Ávila, Mariana G. Mora Sánchez, Alma Sofía Bernal Amador, Ramón Paniagua

    Published 2025-01-01
    “…Serum creatinine levels are the most used clinical marker to estimate renal function as the glomerular function rate because it is simple, fast, and inexpensive. …”
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    Solving Inverse Wave Problems Using Spacetime Radial Basis Functions in Neural Networks by Chih-Yu Liu, Cheng-Yu Ku, Wei-Da Chen, Ying-Fan Lin, Jun-Hong Lin

    Published 2025-02-01
    “…In this study, we propose an innovative framework for solving inverse problems in wave equations by using deep learning techniques with spacetime radial basis functions (RBFs). …”
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  14. 534

    Solution of Second Kind Volterra Integral Equations Using Non-Polynomial Spline Functions by Baghdad Science Journal

    Published 2014-06-01
    “…In this paper we use non-polynomial spline functions to develop numerical methods to approximate the solution of 2nd kind Volterra integral equations. …”
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    Stable and Holomorphic Implementation of Complex Functions Using a Unit Circle-Based Transform by Binesh Thankappan

    Published 2021-01-01
    “…A stable and holomorphic implementation of complex functions in ℂ plane making use of a unit circle-based transform is presented in this paper. …”
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  17. 537

    Hyers–Ulam Stability of Additive Functional Equation Using Direct and Fixed-Point Methods by K. Tamilvanan, G. Balasubramanian, Nazek Alessa, K. Loganathan

    Published 2020-01-01
    “…In this present work, we obtain the solution of the generalized additive functional equation and also establish Hyers–Ulam stability results by using alternative fixed point for a generalized additive functional equation χ∑g=1lvg=∑1≤g<h<i≤lχvg+vh+vi−∑1≤g<h≤lχvg+vh−l2−5l+2/2∑g=1lχvg−χ−vg/2. where l is a nonnegative integer with ℕ−0,1,2,3,4 in Banach spaces.…”
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  18. 538

    Functionalization Effect of Multi-Walled Carbon Nanotubes Used as Supports for Cu-Based Catalysts by Juan S. Morales Giurato, Matías G. Rinaudo, Maria R. Morales

    Published 2025-03-01
    “…The distinctive physicochemical properties achieved in the MWCNT supports could be related to the nature of the functionalization treatment that was used, with consequent effects on the features of the Cu catalysts.…”
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    Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks by Leonardo Ferreira Guilhoto, Paris Perdikaris

    Published 2024-11-01
    “…Abstract Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. …”
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